Using cluster analysis for understanding spatial and temporal patterns and controlling factors of groundwater geochemistry in a regional aquifer

Jian, Yang; Ye, Ming; Tang, Zhonghua; Jiao, Tian; Song, Xiaoyu; Pei, Yongzhen; Liu, Honghua

doi:10.1016/j.jhydrol.2020.124594

Cited by 57 publications

(24 citation statements)

References 52 publications

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“…The "data" folder contains four example datasets including the one shown in Table 2, which are organized into three different formats (Microsoft Excel, CSV, and tab-delimited Text) to demonstrate how to organize and process aqueous geochemical data in the WQChartPydesired format. The other three datasets organized in CSV formats are taken from previous references (Yang et al 2020;Moreno Merino et al 2021;Liu et al 2021b).…”

Section: Wqchartpy Implementationmentioning

confidence: 99%

Visualization of Aqueous Geochemical Data Using Python and WQChartPy

et al. 2022

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Graphical methods have been widely used for visualization, classification, and interpretation of aqueous geochemical data to obtain a better understanding of surface and subsurface hydrologic systems. This method note presents WQChartPy, an open-source Python package developed to plot a total of 12 diagrams for analysis of aqueous geochemical data. WQChartPy can handle various data formats including Microsoft Excel, commaseparated values (CSV), and general delimited text. The 12 diagrams include eight traditional diagrams (trilinear Piper, Durov, Stiff, Chernoff face, Schoeller, Gibbs, Chadha, and Gaillardet) and four recently proposed diagrams (rectangle Piper, color-coded Piper, contour-filled Piper, and HFE-D) that have not been implemented in existing graphing software. The diagrams generated by WQChartPy can be saved as portable network graphics (PNG), scalable vector graphics (SVG), or portable document format (PDF) files for scientific publications. Jupyter and Google Colab notebooks are available online to illustrate how to use WQChartPy with example datasets. The geochemical diagrams can be generated with several lines of Python codes.

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Section: Wqchartpy Implementationmentioning

confidence: 99%

Visualization of Aqueous Geochemical Data Using Python and WQChartPy

et al. 2022

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“…The clusters of GFMM were congruent with the classification carried out by the PCA and Piper diagram. Therefore, the Oslo dataset or similar datasets such as the Taiyuan karst water (Ma et al 2011) and Jianghan Plain groundwater (Yang et al 2020) may only be used as auxiliary indices to assess the performance of a clustering method. However, making a conscious decision on the efficacy of a clustering method also requires the soft knowledge of the practitioner, as is the case for the geochemical dataset of this study and the results obtained from applying GFMM.…”

Section: Strengths and Weaknesses Of Gfmm And Ssb For Clusteringmentioning

confidence: 99%

“…Clustering is considered a propitious approach that has been widely used in several multivariate analyses to delineate spatiotemporal patterns embedded in a groundwater geochemistry dataset (e.g., Cloutier et al 2008; Güler and Thyne 2004a, b; Yang et al 2020; Papaioannou et al 2010; Woocay and Walton 2008). However, the validity of procured geochemical clusters has largely remained a formidable challenge because clustering is a subjective process (Dougherty and Brun 2004).…”

Section: Introductionmentioning

confidence: 99%

Inspecting the Reliability of Geochemical Facies Identified for the Waterworks' Capture Zone in Germany

2022

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Little research attention has been given to validating clusters obtained from the groundwater geochemistry of the waterworks' capture zone with a prevailing lake-groundwater exchange. To address this knowledge gap, we proposed a new scheme whereby Gaussian finite mixture modeling (GFMM) and Spike-and-Slab Bayesian (SSB) algorithms were utilized to cluster the groundwater geochemistry while quantifying the probability of the resulting cluster membership against each other. We applied GFMM and SSB to 13 geochemical parameters collected during different sampling periods at 13 observation points across the Barnim Highlands plateau located in the northeast of Berlin, Germany; this included 10 observation wells, two lakes, and a gallery of drinking production wells. The cluster analysis of GFMM yielded nine clusters, either with a probability ≥0.8, while the SSB produced three hierarchical clusters with a probability of cluster membership varying from <0.2 to >0.8. The findings demonstrated that the clustering results of GFMM were in good agreement with the classification as per the principal component analysis and Piper diagram. By superimposing the parameter clustering onto the observation clustering, we could identify discrepancies that exist among the parameters of a certain cluster. This enables the identification of different factors that may control the geochemistry of a certain cluster, although parameters of that cluster share a strong similarity. The GFMM results have shown that from 2002, there has been active groundwater inflow from the lakes towards the capture zone. This means that it is necessary to adopt appropriate measures to reverse the inflow towards the lakes.

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“…However, Na + /Clmolar ratio in the majority of samples (67%) is higher than unity (average = 1.4). This indicates that there are other sources of Na such as silicate weathering or cation exchange (Yang et al 2020). Webster et al (1994) introduced the ratios of Mg 2+ /Na + and Mg 2+ /Ca 2+ as an indication of the soil salt leaching and evaporation in arid environments.…”

Section: Origin Of Salts In Groundwatermentioning

confidence: 99%

Hydrogeochemical characteristics and source identification of salinity in groundwater resources in an arid plain, northeast of Iran: implication for drinking and irrigation purposes

Taherian¹,

Joodavi²

2021

AS-ITJGW

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Groundwater salinization is a worldwide problem where groundwater is the principal source of water. A combination of geochemical and statistical approaches was used to investigate the mechanisms governing the groundwater chemistry and origin of salts in the Neyshabour aquifer (north-eastern Iran). The mean values of Mg2+ (61.4 mg/L), Na+ (553.2 mg/L), Cl- (800.4 mg/L), SO4 2- (428.7 mg/L), EC (3404 μS/cm), TH (525.0 mg/L) and TDS (2212.8 mg/L) in 55 groundwater samples taken from deep wells were higher than WHO and ISIRI guideline values. Geochemical and multivariate statistical analysis suggested that: i) the dissolved solids in the water samples are controlled mainly by geology, and ii) Na-Cl and Na-HCO3 type waters are dominant in the area. Besides the water–rock reactions (e.g., evaporites dissolution), groundwater salinity in Neyshabour aquifer has been intensified by irrigation return flows and groundwater level decline. The chemical weathering of mafic and ultramafic rocks in ophiolitic rocks is responsible for Mg enrichment in the majority of samples. Water quality index (WQI) and different indices calculated for the groundwater samples indicated that the most of them have poor water quality for drinking and agricultural uses especially in the southern, central and western parts of the plain.

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Using cluster analysis for understanding spatial and temporal patterns and controlling factors of groundwater geochemistry in a regional aquifer

Cited by 57 publications

References 52 publications

Visualization of Aqueous Geochemical Data Using Python and WQChartPy

Visualization of Aqueous Geochemical Data Using Python and WQChartPy

Inspecting the Reliability of Geochemical Facies Identified for the Waterworks' Capture Zone in Germany

Hydrogeochemical characteristics and source identification of salinity in groundwater resources in an arid plain, northeast of Iran: implication for drinking and irrigation purposes

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